Prediction of Thrombectomy Functional Outcomes using Multimodal Data

Recent randomised clinical trials have shown that patients with ischaemic stroke {due to occlusion of a large intracranial blood vessel} benefit from endovascular thrombectomy. However, predicting outcome of treatment in an individual patient remains a challenge. We propose a novel deep learning approach to directly exploit multimodal data (clinical metadata information, imaging data, and imaging biomarkers extracted from images) to estimate the success of endovascular treatment. We incorporate an attention mechanism in our architecture to model global feature inter-dependencies, both channel-wise and spatially. We perform comparative experiments using unimodal and multimodal data, to predict functional outcome (modified Rankin Scale score, mRS) and achieve 0.75 AUC for dichotomised mRS scores and 0.35 classification accuracy for individual mRS scores.Comment: Accepted at Medical Image Understanding and Analysis (MIUA) 202

A Delphi study and ranking exercise to support commissioning services:Future delivery of Thrombectomy services in England

Background: Intra-arterial thrombectomy is the gold standard treatment for large artery occlusive stroke. However, the evidence of its benefits is almost entirely based on trials delivered by experienced neurointerventionists working in established teams in neuroscience centres. Those responsible for the design and prospective reconfiguration of services need access to a comprehensive and complementary array of information on which to base their decisions. This will help to ensure the demonstrated effects from trials may be realised in practice and account for regional/local variations in resources and skill-sets. One approach to elucidate the implementation preferences and considerations of key experts is a Delphi survey. In order to support commissioning decisions, we aimed to using an electronic Delphi survey to establish consensus on the options for future organisation of thrombectomy services among physicians with clinical experience in managing large artery occlusive stroke. Methods: A Delphi survey was developed with 12 options for future organisation of thrombectomy services in England. A purposive sampling strategy established an expert panel of stroke physicians from the British Association of Stroke Physicians (BASP) Clinical Standards and/or Executive Membership that deliver 24/7 intravenous thrombolysis. Options with aggregate scores falling within the lowest quartile were removed from the subsequent Delphi round. Options reaching consensus following the two Delphi rounds were then ranked in a final exercise by both the wider BASP membership and the British Society of Neuroradiologists (BSNR). Results: Eleven stroke physicians from BASP completed the initial two Delphi rounds. Three options achieved consensus, with subsequently wider BASP (97%, n=43) and BSNR members (86%, n=21) assigning the highest approval rankings in the final exercise for transferring large artery occlusive stroke patients to nearest neuroscience centre for thrombectomy based on local CT/CT Angiography. Conclusions: The initial Delphi rounds ensured optimal reduction of options by an expert panel of stroke physicians, while subsequent ranking exercises allowed remaining options to be ranked by a wider group of experts within stroke to reach consensus. The preferred implementation option for thrombectomy is conveying suspected stroke patients for CT/CT Angiography and secondary transfer of large artery occlusive stroke patients to the nearest neuroscience centre